class_docstr: Test the CVScores visualizer
docstr: Test image closeness on a classification dataset with kNN
docstr_tok: ["Test","image","closeness","on","a","classification","dataset","with","kNN"]
code: def test_classifier(self):
      """
      Test image closeness on a classification dataset with kNN
      """
      X, y = self.classification
  
      cv = ShuffleSplit(3, random_state=288)
  
      oz = CVScores(
          KNeighborsClassifier(), cv=cv, scoring="f1_weighted"
      )
  
      oz.fit(X, y)
      oz.finalize()
  
      self.assert_images_similar(oz, tol=2.0)
  
imports: import pytest
  import numpy.testing
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import SVC
  from sklearn.naive_bayes import BernoulliNB
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.linear_model import RidgeCV, LogisticRegressionCV
  from sklearn.model_selection import ShuffleSplit, StratifiedKFold
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom
  from yellowbrick.model_selection.cross_validation import *
label_desc: Use image node_id 2 for calling the TestCrossValidation obj's underlying member method code with example usage: obj.test_classifier() without return types